Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

XIAOJIA HUANG^1,#, CAILU SONG^2,#, JINHUI ZHANG², LEWEI ZHU^3,*, HAILIN TANG^2,*

Oncology Research, Vol.32, No.2, pp. 241-249, 2024, DOI:10.32604/or.2023.046582 - 28 December 2023

Abstract Breast cancer has surpassed lung cancer to become the most common malignancy worldwide. The incidence rate and mortality rate of breast cancer continue to rise, which leads to a great burden on public health. Circular RNAs (circRNAs), a new class of noncoding RNAs (ncRNAs), have been recognized as important oncogenes or suppressors in regulating cancer initiation and progression. In breast cancer, circRNAs have significant roles in tumorigenesis, recurrence and multidrug resistance that are mediated by various mechanisms. Therefore, circRNAs may serve as promising targets of therapeutic strategies for breast cancer management. This study reviews the More >

Muhammad Mumtaz Ali, Faiqa Maqsood, Shiqi Liu, Weiyan Hou, Liying Zhang, Zhenfei Wang^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2699-2714, 2023, DOI:10.32604/cmc.2023.045310 - 26 December 2023

Abstract Breast cancer, particularly Invasive Ductal Carcinoma (IDC), is a primary global health concern predominantly affecting women. Early and precise diagnosis is crucial for effective treatment planning. Several AI-based techniques for IDC-level classification have been proposed in recent years. Processing speed, memory size, and accuracy can still be improved for better performance. Our study presents ECAM, an Enhanced Channel-Wise Attention Mechanism, using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma (BIDC). The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture, improve data representation through hierarchical feature… More >

Jiaji Wang¹, Muhammad Attique Khan², Shuihua Wang^1,3, Yudong Zhang^1,3,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2201-2216, 2023, DOI:10.32604/cmc.2023.041191 - 30 August 2023

Abstract Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train… More >

EFFAT ALEMZADEH¹, LEILA ALLAHQOLI², HAMIDEH DEHGHAN³, AFROOZ MAZIDIMORADI⁴, ALIREZA GHASEMPOUR³, HAMID SALEHINIYA^5,*

Oncology Research, Vol.31, No.5, pp. 667-675, 2023, DOI:10.32604/or.2023.028406 - 21 July 2023

Abstract Liquid biopsy, including both circulating tumor cells and circulating tumor DNA, is becoming more popular as a diagnostic tool in the clinical management of breast cancer. Elevated concentrations of these biomarkers during cancer treatment may be used as markers for cancer progression as well as to understand the mechanisms underlying metastasis and treatment resistance. Thus, these circulating markers serve as tools for cancer assessing and monitoring through a simple, non-invasive blood draw. However, despite several study results currently noting a potential clinical impact of ctDNA mutation tracking, the method is not used clinically in cancer More > Graphic Abstract

Alia Alshehri, Duaa AlSaeed^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096 - 29 April 2023

Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening… More >

Erkan Akkur¹, Fuat TURK^2,*, Osman Erogul¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003 - 03 November 2022

Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast… More >

Yassir Edrees Almalki¹, Maida Khalid², Sharifa Khalid Alduraibi³, Qudsia Yousaf², Maryam Zaffar², Shoayea Mohessen Almutiri⁴, Muhammad Irfan⁵, Mohammad Abd Alkhalik Basha⁶, Alaa Khalid Alduraibi³, Abdulrahman Manaa Alamri⁷, Khalaf Alshamrani⁸, Hassan A. Alshamrani^8,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4103-4121, 2022, DOI:10.32604/cmc.2022.029039 - 16 June 2022

Abstract Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be… More >

José Escorcia-Gutierrez^1,*, Romany F. Mansour², Kelvin Beleño³, Javier Jiménez-Cabas⁴, Meglys Pérez¹, Natasha Madera¹, Kevin Velasquez¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4221-4235, 2022, DOI:10.32604/cmc.2022.022322 - 14 January 2022

Abstract Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated… More >

G. Jayandhi^1,*, J.S. Leena Jasmine², S. Mary Joans²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 491-503, 2022, DOI:10.32604/csse.2022.016376 - 09 September 2021

Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces… More >